The field of machine learning has become widely acknowledged as a likely significant driver of the future of technology. Organizations everywhere now seek to use machine learning techniques to address a wide variety of problems, such as optimizing aspects of their products, processes, customer experience, etc. While the high-level view of machine learning sounds simple—e.g., provide training data to a computer, to allow the computer to automatically learn from the training data to generate a model that can make predictions for other data—implementing machine learning techniques in practice can be tremendously difficult.
This difficulty is partially due to the underlying algorithmic and mathematical complexities of machine learning algorithms, which are typically developed by academic researchers or individuals at the forefront of the field. Additionally, it is also difficult to generate, update, and deploy useful models, which can be extremely time and resource consumptive and filled with complexities. Moreover, machine learning models tend to be extremely focused on particular use cases and operating environments, and thus any change to the underlying environment or use case may require a complete regeneration of a new model. Further, constructing and deploying machine learning technologies is quite different from traditional software engineering, and requires practices and architectures different from what traditional software engineering development teams are familiar with.